Real-time automated classification system

ABSTRACT

The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/128,124, entitled “REAL-TIME AUTOMATEDCLASSIFICATION SYSTEM,” filed Sep. 11, 2018, which is herebyincorporated by reference in its entirety for all purposes.

BACKGROUND

The present disclosure relates generally to the capturing of human inputand actions as metadata in the creation of broadcast and video contentand, more particularly, to machine learning of real-time data loggingassociated with broadcast and video content.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present techniques,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

Exclusive logging systems are used to collect data across the manyplatforms used in the creation of broadcast content and other videocontent for tracking, recording, and monitoring associated with thecreation and manipulation of the content. These logging systems collecta tremendous among of log data related to the broadcast content, forexample. In particular, log data may include data from various devicesor systems used during the creation of broadcast content. This data israrely accessed because log data is principally used by engineers todetermine faults and errors. Traditionally, broadcast content isanalyzed manually (e.g., by human analysts) to identify significantelements in the broadcast content. Based on the analysis, metadatafields are entered manually into predetermined fields, and suitableedits may be manually determined and/or added to generate enhancedbroadcast content. For example, the audiences may receive the broadcastcontent with added captioning, added video clips, added commentaries,additional links, etc. to complement the broadcast content. However, thetraditional manual approach to characterize broadcast content and/orproduce the edited broadcast content may be labor intensive,time-consuming, inconsistent, and inefficient.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the originally claimedsubject matter are summarized below. These embodiments are not intendedto limit the scope of the claimed subject matter, but rather theseembodiments are intended only to provide a brief summary of possibleforms of the subject matter. Indeed, the subject matter may encompass avariety of forms that may be similar to or different from theembodiments set forth below.

The current embodiments relate to systems and methods for real-timeautomated classification of content (e.g., broadcast content or othervideo content) using existing data, such as data from various loggingsystems. For example, the current embodiments relate to a real-timeautomated classification system, where a machine learning system is usedto recognize important moments in broadcast content based on log dataand/or other data received from various classification systems. In anaspect, the real-time automated classification system may be trained torecognize correlations between the various log data to determine what ishappening in the broadcast content (e.g., a host/anchor is speaking, ascoring moment in a sports game). Specifically, the real-time automatedlogging system may determine and generate metadata that describe or giveinformation about what is happening or appearing in the broadcastcontent. In addition, the real-time automated classification system mayautomatically generate control inputs, suggestions, recommendations,and/or edits relating to broadcast content (e.g., supplemental elementsto be incorporated in the live broadcast, such as graphic elements,automated clips, reports, analyses, texts, captions, labels, and anyother suitable supplemental elements). Accordingly, the real-timeautomated classification system may generate broadcast content inreal-time more efficiently and effectively (e.g., using log informationfrom one or more classification systems).

DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic view of a real-time automated classificationsystem for broadcast content, in accordance with an embodiment of thepresent disclosure;

FIG. 2 is a flow diagram illustrating a process in which the real-timeautomated classification system of FIG. 1 provides automated control,automated suggestions, and/or automated reporting relating to thebroadcast content, in accordance with an embodiment of the presentdisclosure;

FIG. 3A and FIG. 3B illustrate a table illustrating the correlation oflog data with a tally log based on the master timing provided by thetally log, in accordance with an embodiment of the present disclosure.

FIG. 4 is a schematic illustrating a format of a machine-learningobservation data for factorization of observations of broadcast content,in accordance with an embodiment of the present disclosure;

FIG. 5 is a schematic illustrating an example of the real-time automatedclassification system of FIG. 1 providing an automated suggestionrelating to broadcast content, in accordance with an embodiment of thepresent disclosure;

FIG. 6 is a schematic illustrating an example of the real-time automatedclassification system of FIG. 1 providing an automated clip relating tobroadcast content, in accordance with an embodiment of the presentdisclosure;

FIG. 7 is a schematic illustrating an example of the real-time automatedclassification system of FIG. 1 providing an automated report relatingto broadcast content, in accordance with an embodiment of the presentdisclosure;

FIG. 8 is a schematic illustrating an example of the real-time automatedclassification system of FIG. 1 providing an automated control relatingto broadcast content, in accordance with an embodiment of the presentdisclosure; and

FIG. 9 is a schematic illustrating an example of the real-time automatedclassification system of FIG. 1 providing an automated text filerelating to broadcast content, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

One or more specific embodiments of the present disclosure will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

As set forth above, there exists an opportunity to more efficiently logdata and provide control inputs, suggestions, and/or edits to broadcastcontent. By machine learning of log data relating to creation ofbroadcast content, various processes of generating the content (e.g.,broadcast content) may be automated, rather than performed manually,resulting in significant time-savings. Indeed, the time efficienciesprovided by the current techniques may result in new offerings that areimpossible to provide in time-sensitive broadcasting. For example,future consumer demand may require the ability to adapt live broadcastcontent into digital formats for viewing. Moreover, in certain types ofbroadcasts, such as live broadcasts, there is a limited amount of timeavailable to edit content before the content is to be broadcasted. Byperforming real-time and/or rapid classification of content, crucialtime is saved, enabling additional and/or better storytelling,journalism, and overall compilation of the broadcast content.Accordingly, a real-time automated classification system in accordancewith the present embodiments may substantially reduce the time it takesto process a tremendous amount of log data and to improve the efficiencyof compiling broadcast content as well as other broadcast enterprisefunctions related to the creation of assets, resulting in higher qualitybroadcasts with more enhanced output of content.

Turning now to a more detailed discussion of the real-time automatedclassification system, FIG. 1 is a schematic view of a real-timeautomated classification system 10, in accordance with an embodiment ofthe present disclosure. As illustrated, the real-time automatedclassification system 10 may be a cloud-based system and may include areal-time automated classification interface 12 and a control system 14communicatively coupled to the real-time automated classificationinterface 12. The real-time automated classification interface 12 mayinclude suitable wired and/or wireless communication interfacesconfigured to receive log data 16 from logging systems 18. The log data16 may include a tremendous amount of data generated during creation ofvideo content, such as during a live broadcast event (e.g., sports game,talk show, news reporting).

In general, the log data 16 include data from data loggers that logoperations of devices and/or systems (e.g., character generators (CGs),newsroom computer systems, prompter systems, audio consoles, real-timegraphics generators (e.g., generators of charts and/or other datarepresentations), transmission systems, and lighting systems)contributing to capturing or broadcasting video content of an event. Thelog data 16 may include, but are not limited to, time code information,text information, system state data, audio console log(s), real-timegraphic system log(s), tally log(s), switcher log(s), news room controlsystem log(s) (e.g., iNews logs), Media Object System (MOS) gatewaylog(s), video replay log(s), and/or video playout log(s). For example,given the extended recording times of data loggers, there is typically amechanism to record the date and time in a timestamp to ensure that eachrecorded data value has a timecode reference with a date and time ofacquisition in order to produce a sequence of events. For example, “textinformation,” such as a text log or a text file may be used to log eventinformation (e.g., name of the log file including wildcard characters asappropriate). For example, “audio log(s)” may include physical actionstaken by the audio mixer, microphone groupings (or individualmicrophones) that are open or closed, decibel level, and sound files. Asanother example, “real-time graphic system log(s)” may include stockand/or commodity symbols, time durations, prices, and formats ofrequested graphics. As a further example, “tally log(s)” include dataindicative of the On-Air status of one or more devices (e.g., camera,individual/multiple CG channel, real-time graphic sources, videoplayout, AUX input, transmission source, solid state graphics playoutsystems). Tally logs may be generated by a tally system 19, which maycontrol the tally light and associated indicators that appear on devicesas well as multi-viewers associated with the control system 14. As yetanother example, “switcher log(s)” provide information on the activitiesof the technical director, who is responsible for implementing thedecisions of the director. More specifically, switcher logs may includedata indicative of putting specific cameras, graphics, and transitionson-air. For example, “newsroom system log(s)” may include the rundown,script of a television program, any self-serve graphic information, CGformat and text, real-time graphic orders, segment partitions,host/guest information, playout sources and graphic information that areinput by a producer or director for each broadcast segment. For example,“Media Object Server (MOS) gateway logs” will display when specificdevices are invoked and rendered based on commands of an operator. Forexample, replay log(s) may include data relating to video “instant”replay of broadcast content. For example, “character generator log(s)”may include data indicative of television graphics that occupy the lowerarea of the screen or any predominantly text-based video graphic as usedby television broadcasts. For example, “video playout log(s)” mayinclude data indicative of operation of playout system(s) that provideindications of when file-based video packages with sound or silentB-roll were utilized during the live broadcast. It should also be notedthat the real-time automated classification interface 12 may beconfigured to receive and accumulate the log data 16 from differenttypes of logging systems 18 and log data 16 from different manufacturersor platforms.

The real-time automated classification interface 12 may include a memory20 (e.g., a tangible, non-transitory, machine-readable medium) thatstores machine readable instructions and may include a processor 22configured to execute these instructions to control various componentsof the real-time automated classification system 10. While theillustrated embodiment is described as including the processor 22, inother embodiments, the real-time automated classification interface 12may include several processors that are each configured to executeinstructions stored on the memory 22. The real-time automatedclassification interface 12 may receive and analyze the log data 16. Insome embodiments, the real-time automated classification interface 12may include a machine learning module or system 24 (e.g., stored in thememory 20). The machine learning module 24 may include any suitablemachine learning algorithms to perform supervised learning orsemi-supervised learning. Once the machine learning module 24 is trainedbased on video content, current broadcast content, and/or previouslyrecorded broadcast content, the real-time automated classificationinterface 12 may provide automated control, automated suggestions,and/or automated reporting 17 based on the log data 16. For example, inthe current embodiment, the real-time automated classification interface12 provides automated control, automated suggestions, and/or automatedreporting 17 to the control system 14, which may be used to facilitatebroadcast content compilation, create derivative content, and generatemultiple “tracks” of graphics output.

By automating the provision of automated control, automated suggestions,and/or automated reporting 17 to the control system 14, time-consumingtasks that have typically required significant human subjectivity can bereduced. For example, automatic topical classification of content may beattributed to content, and automatic B-roll content recommendations,etc. may be provided to the control system 14 in a time-efficient mannernot previously seen in this industry. This may result in higher-qualitycontent editing, increased ability to produce live content, and bettertailoring of the content to target audiences, etc.

FIG. 2 is a flow diagram illustrating a process 30 in which thereal-time automated classification system 10 provides automated control,automated suggestions, and/or automated reporting based on the log data16. One or more operations of the process 30 may be executed by theprocessor 22 of the real-time automated classification interface 12.Referring to the real-time automated classification system 10 of FIG. 1, the process 30 may include (operation 32) accumulating the log data16. The real-time automated classification interface 12 may receive andaccumulate the log data 16 from the logging systems 18. The log data 16includes data or information relating to particular devices that areobserved during generation of content (e.g., broadcast content). Forexample, as discussed above, the log data 16 may include, but are notlimited to, time code information, text information, audio log(s),real-time graphic system log(s), tally log, switcher log(s), newsroomcontrol system log(s), MOS gateway logs, video replay log(s) and videoplayout log(s).

The process 30 may include characterizing and/or correlating (operation34) the log data 16. As set forth above, there may be a tremendousamount of log data 16 created during broadcasting content. As such, thereal-time automated classification interface 12 may correlate the logdata 16, based on a master source, such as data included in the tallylogs. The real-time classification interface 12 may correlate any otherlog data 16 with the tally logs, and the resulting correlated data canbe utilized for the operations in the process 30 described below.Correlating the log data 16 with the tally logs ensures that all of theevents are captured for a particular moment in time.

Moreover, correlating the log data 16 with the tally logs enables theimplementation of a higher-level taxonomy, which allows a heuristicalgorithm to label particular groups of actions. For example, one suchgrouping might be a “four-box” with lower-third. The “four-box” mayrepresent a switcher output configuration that shows four live camerason a single screen (e.g., one camera displayed on each of the upperleft, upper right, lower left, and lower right). The lower-third may bea textual graphic element that may describe the context of the on-screenaction, in which the textual graphic element appears in the lower thirdportion of the screen. These action groups and their associated labels,enable the machine learning module 24 to perform actions based on thepresence of a labeled group. For instance, the machine learning module24 may use labeled data (e.g., tagged data) to teach the processor 22how to discover important event groups.

For example, the correlated data may be run through a heuristicalgorithm (e.g., an algorithm included in the machine learning module24), which may tag critical data elements of the log data 16. Thesecritical data elements may represent noteworthy actions within thebroadcast. Additionally, the tagged data may be characterized in termsof their types, e.g., time code information, text information, audiolog, real-time graphic system log, tally log, switcher log, newsroomcontrol system log, MOS gateway log, video replay log, charactergenerator and video playout log. The tagged data may be mapped into ahigher level ontology which would provide more actionable information.For example, the ontology may map a series of correlated log entriesinto higher-level terms.

The real-time automated classification interface 12 may characterize thelog data 16 in terms of their points of origination, e.g., from acamera, from a microphone, from a graphics system, from a newsroomsystem, from a light system, from an audio system. The log data 16 mayalso be categorized according to a particular device, e.g., from camera#1, from camera #2, from camera #3, from microphone #1, from microphone#2, from microphone #3. Additionally, the log data 16 may becharacterized in terms of one or more formats associated with aparticular device, e.g., a video file format, an audio file format, atext format. Furthermore, the log data 16 may be characterized in termsof time information associated with a particular device, e.g., thespecific time of day when content (e.g., particular frames of content)was broadcasted and elements that were created and brought to air. Thelog data 16 may also be characterized by the size of the data. Moreover,the log data 16 may be characterized by the relative importance and/oraccess frequency of the data, e.g., data frequently analyzed versus datararely analyzed in a post analysis, data relating to an anchor or showhost versus data relating to audiences, data relating to a scoringmoment of a sports game. In some embodiments, the log data 16 may bepre-characterized by the machine learning module 24. In someembodiments, the log data 16 may be pre-characterized by authorizedusers or personnel (e.g., human loggers).

There may exist correlations or relationships between the log data 16.For example, there may be a correlation or relationship betweendifferent log data 16 of different types, e.g., the tally log maystrongly correlate to the newsroom system log, the switcher log maystrongly correlate with graphics log and/or character generator log, andthe time code information may strongly correlate to video playout log.There may be correlation by log event timing, which groups events thatoccur at the same (or around the same) time. For instance, as describedabove, tally log data 16 a provides the master timing function, and anyother log data may be correlated to this timing. There may be acorrelation or relationship between the log data 16 from a particulardevice and the relative importance of the data, e.g., the log data 16from a camera #1 may strongly correlate to activity of an anchor or showhost, the log data 16 from microphone #4 may strongly correlate to voicefrom guests or audiences on broadcast content. There may be acorrelation or relationship between the time information and therelative importance of the data, e.g., the log data 16 logged duringmiddle of a sports game may more likely correlate to a scoring momentthan the log data 16 logged at the very beginning of the sports game. Insome embodiments, the correlations or relationships between the log data16 may be determined by the machine learning module 24. In someembodiments, the correlations or relationships between the log data 16may be pre-defined by authorized users or personnel (e.g., by humanloggers).

To further illustrate the characterization of log data 16 andcorrelation of the log data 16 with the tally log, FIG. 3A and FIG. 3Bare provided. In particular, FIG. 3A and FIG. 3B are a table 41illustrating the correlation of log data 16 with the tally log based onthe master timing provided by the tally log. As illustrated, the table41 includes several rows 42 and columns 43. More specifically, each row42 is associated with particular events at different times. For example,row 42 a is associated with a first action or segment of content, whileeach subsequent row 42 is associated with content that occurs at a timeafter the content associated with the row 42 a. Each of the rows 42 maycorrespond to an action group. For instance, as discussed below,different types of log data 16 may be included in a row 42, indicatingthat the log data 16 correlates to one another.

Each of the columns 43 provides information regarding respective columns42 of the table 41. For instance, column 43 a corresponds to eventsdefined in the tally log, which may be referred to as “tally events.”Column 43 b includes a time and frame associated with a tally event.More specifically, the times and frames indicated in the column 43 b aremaster times with which log data 16 is correlated. Column 43 c describesoperator actions associated with the times and frames indicated incolumn 43 b. Column 43 d includes content that is aired at the timeindicated by the column 43 b. Column 43 e indicates a category of data(e.g., a type of log file that data is included in), and column 43 findicates log data 16 associated with the category of data from column43 e at the time indicated by column 43 b. For example, the log data 16may be data included in the log indicated by the column 43 e. Similarly,column 43 g indicates another category of log data 16 (e.g., a type oflog data that is different than a type of log indicated by column 43 e),and column 43 h indicates log data 16 associated with the category ofdata indicated by column 43 g at the time indicated by column 43 b.

With this in mind, several of the tally events associated with the rows42 will be discussed. Row 42 a is associated with a portion of abroadcast. For instance, at the time indicated by box 44 a, the contentin box 44 b may be broadcasted. That the content was broadcasted isindicated in the log data 16 of box 44 c that is associated with thetally log, as indicated by box 44 d. For example, the “CAM-01 is now onair in PCR1” indicates that camera #1 was live in the production controlroom 1, and “CAM-01 is now on air” indicates that the content of box 44b, which was captured from camera #1, was broadcasted. Morespecifically, the log data 16 also indicates the specific time and frameassociated with each item of data.

During the broadcast, different content may be broadcasted. For example,inserts or graphics that are included in the content may be added,changed, or removed. Referring now to row 42 b, at the time indicated bybox 45 a, a graphic 46 a has been included in the broadcast, asillustrated in box 45 b. The inclusion of the graphic 46 a is associatedwith several operator actions indicated by box 45 c. In particular, thegraphic 46 a may be a CG message that was called from a list serve.

Log data 16 associated with the graphic 46 a is also indicated in row 42b. More than one type of log data 16 being included in a row 42, such asrow 42 b, is indicative of the types of log data 16 being correlated.For instance, the log data 16 from the tally log (indicated by box 45 d)indicates that the graphic 46 a, which is associated with a charactergenerator, was brought to air at the time indicated in box 45 d.Additionally, log data 16 associated with a character generator log,shown in box 45 e, indicates that a new story was added, that thegraphic 46 a was called for (e.g., from the list serve), that an ID wasassigned to the story, and that the file for the graphic 46 a wasretrieved. Although the log data 16 from the CG log is associated with atimes before and after the time associated with the log data 16 of thetally log in box 45 d, which is also the time indicated by box 45 b, thereal-time automated classification interface 12 may characterize the logdata 16 from the CG log and tally log indicated in row 42 b as beingcorrelated to one another. For example, as discussed above, the log data16 from the CG log is indicative of the graphic 46 a being called from alist serve, and the log data 16 from the tally log indicates that thegraphic 46 a was aired. In other words, the log data 16 from the CG loghas been associated with a time indicated by the tally log, and, asnoted above, the time from the tally log provides the master time.

Similar to box 46 a, box 47 a shows that new content (e.g., “techdrivers”) aired. As indicated by box 47 b, the tally log includes dataindicating the source of the new content.

As content or the source or content changes, the tally log may reflectthe changes in the log data 16 of the tally log. For example, contentcaptured from another camera or source may be used in addition to, orinstead of, a camera or source previously used. For instance, asillustrated in box 48 a, a split screen is utilized to show people whoare filmed from two different sources. Moreover, as shown in box 48 b,the log data 16 from the tally log indicates that content was aired fromtwo sources (i.e., camera #1 and an address of a router switcher (RS11)associated with a remote source). The log data 16 also indicates thatthe graphic 46 a was aired, as shown in the content of box 48 a.

As another example of correlating log data 16 to the master timeprovided by the tally log, the content of box 49 a corresponds to achange in content being aired. More specifically, graphic 46 b andgraphic 46 c were added to the broadcast content. As indicated in box 49b, the CG log includes log data 16 indicating that a story has beengenerated and assigned an ID. The CG log data 16 b also indicates textincluded in the graphic 46 b and graphic 46 c as well as the filelocator for the background of the graphics 46 b and 46 c. Moreover, asindicated by the log data 16 from the CG log being included in the row42 e with the tally log data 16 a (shown in box 49 c), CG log data 16 bhas been correlated with the tally log data 16 a, which indicates thatthe graphic 46 b and graphic 46 c were brought to air.

As a further example of correlating log data 16, the content of box 50 adepicts that a graphic 46 d has been added to the content. Similar tothe previous example, log data 16 from the CG log has been correlatedwith log data 16 from the tally log. For example, log data 16 from theCG log is indicative of the graphic 46 d (e.g., text included in thegraphic 46 d and file locator(s) for the graphic 46 d). The log data 16from the tally log indicates that the graphic 46 d was aired.

As yet another example of correlating log data 16, the content of box 51a corresponds to a change in content being aired. In particular, agraphic 46 e has been added to the broadcast content. As indicated inbox 51 b, log data 16 from a real-time graphics generator indicates thata stock chart (e.g., graphic 46 e) has been ordered (e.g., for additioninto the broadcast content). The tally log data 16 a included in box 51c indicates that the real-time graphics generator was brought to air.Additionally, as indicated by the log data 16 from the real-timegraphics generator log and tally log being included in the row 42 g, thelog data 16 from the real-time graphics generator log has beencorrelated with tally log data 16 a. For instance, the tally log data 16a and log data from the real-time graphics generator log may becorrelated to the master time indicated within box 51 d. Moreover,because the tally log data 16 a indicates that the real-time graphicsgenerator was brought to air at the time indicated in box 51 d, and thelog data 16 from the real-time graphics generator indicates the content(e.g., graphic 46 e) associated with the real-time graphics generator atthe time indicated in box 51 d, the row 42 g generally indicates thatthe graphic 46 e was aired at the time indicated by box 51 d.

The examples of correlated data discussed above with regard to FIG. 3Aand FIG. 3B largely relate to cases in which one set of log data 16(e.g., a CG log) indicates information about content (e.g., dataregarding graphics 46 a-46 d) and another set of log data 16 (e.g., atally log) indicates that the content was aired. However, it should benoted that the real-time automated classification system 10 maydetermine correlations between other types of log data 16 as well ascorrelations between more than two types of log data. For example, acharacter generator may be used to add one graphic to broadcast contentat the same or a similar time as when another real-time graphicsgenerator adds another graphic to the broadcast content. In such a case,log data 16 from a CG log data, a real-time graphic system log, and atally log may be correlated to one another. Additionally, the real-timeautomated classification system 10 may determine correlations betweenlog data 16 that are not apparently related to or apparently correlatedto one another. For example, the real-time automated classificationsystem 10 may determine correlations that a human is unable to discover.

Referring back to FIG. 2 , the process 30 may include performing(operation 36) machine learning on the characterized and/or correlatedlog data 16. The machine learning module 24, when executed by theprocessor 22, may be configured to automatically perform various tasksassociated with the machine learning module 24 to perform validationand/or training of the machine learning module 24. For example, themachine learning module 24 may be trained on training data to improveaccuracies of characterizing the log data 16 and determiningcorrelations or relationships between the characterized log data 16. Thetraining data may include previous or historical log data 16 associatedwith previous or historical broadcast content. The training data mayinclude inputs or updates provided by the authorized users or personnel.In some embodiments, the log data 16 may come from devices and/orsystems from different manufacturers or platforms, and the machinelearning module 24 may be trained to more accurately characterize thelog data 16.

The process 30 includes deriving (operation 38) metadata 39 related tothe creation of the broadcast content. The metadata 39 may includedescriptions of or give information about what is happening in thebroadcast content. In particular, the machine learning module 24, whenexecuted by the processor 22, may be configured to automatically performvarious tasks associated with the machine learning module 24 to generatethe metadata 39 for the corresponding live or pre-taped broadcastcontent. For example, the machine learning module 24 may be configuredto generate the metadata 39 based on the learned characterized and/orcorrelated log data 16. For instance, referring briefly to FIG. 3A andFIG. 3B, the metadata 39 may indicate, among other things, broadcastcontent that included one or more characteristics. For instance, themetadata may indicate content that a person (e.g., a host or guest)appeared. For example, the metadata may indicate that a guest appeared(e.g., in content associated with box 48 a) and/or the specific guestthat appeared. As another example, the metadata may indicate that thebroadcast content included graphics (e.g., graphics 46 a-e) or specificcharacteristics of the graphics 46 a-e themselves. For instance,metadata may be generated to indicate the graphic 46 e is a stock chart,includes information regarding a specific company, and/or a time periodassociated with the stock chart (e.g., particular dates and/or an amountof time). As yet another example, the metadata may indicate thatparticular log data 16 is correlated to other log data 16 as well as howthe log data 16 are correlated to one another (e.g., correlated by atime defined by the tally log).

Referring back to FIG. 2 , the process 30 includes performing (operation40) automated control, automated suggestions, and/or automated reportingbased on the metadata 39. The machine learning module 24, when executedby the processor 22, may be configured to automatically generate controlinputs, suggestions, and/or reports for the broadcast content, forexample, based on the derived metadata 39 in operation 38. For example,the machine learning module 24 may enhance the generation of and/orselection of graphic elements, clips, reports, analyses, texts,captions, labels, control inputs, etc. The real-time automatedclassification system 10 may distribute the generated control inputs,suggestions, and/or reports to suitable receiving systems. For example,depending on content of the control inputs, suggestions, and/or reports,the real-time automated classification system 10 may broadcast thecontrol inputs, suggestions, and/or reports along with the broadcastcontent, distribute the control inputs, suggestions, recommendations,and/or edits to authorized personnel (e.g., a producer, an editor, ashow host or anchor of the broadcast content), or both. In someembodiments, the generated control inputs, suggestions, and/or reportsare reviewed and/or approved prior to being broadcasted and/ordistributed. For example, the real-time automated classification system10 may receive an approval from an authorized personnel regarding thecontent of the generated control inputs, suggestions, and/or reports,and in response to receiving the approval, the real-time automatedclassification system 10 may broadcast and/or distribute the generatedcontrol inputs, suggestions, and/or reports. In some embodiments, theautomated control, automated suggestions, and/or automated reportingperformed based on the metadata performed in operation 38 may be addedto training data of the machine learning module 24. As such, the machinelearning module 24 may be trained and/or updated to (e.g., as indicatedby an arrow 42) to improve the accuracy of characterizing and/orcorrelating the log data 16, which in turn may result in improved (e.g.,more accurate) prediction/generation of automated control, automatedsuggestions, and/or automated reporting in operation 40. For example,the machine learning module 24 may determine, as a form of feedback,whether suggested content/input is accepted by a user. If suggestedcontent/input based on certain observations (e.g., O1 and/or O2) areoften accepted, then those content/input and/or correspondingobservations may be more relevant and assigned greater weight values,for example. By contrast, if suggested content/input based on theobservations are rarely, if ever, accepted, then those suggestions maybe less meaningful, and the suggestions and/or observations may beassigned smaller weight values or eliminated in the future.

As discussed in detail herein, the metadata 39 generated in operation 38and the performed control, suggestion, and/or reporting in operation 40of FIG. 2 may be determined in part or in whole based upon machinelearning. The metadata 39 may have any suitable format to describe orgive information about what is happening in the broadcast content. Toperform the machine-learning, observation data may be used to enable themachine to make predictions regarding the broadcast content, such thatthe metadata 39 may be derived and/or to make predictions regardingparticular actions to perform, suggestions to provide, generatederivative content and/or alternate graphics, or report data to provide.

FIG. 4 is a schematic illustrating an example format of amachine-learning observation data 52, which provides factorizedobservations of the broadcast content that may be generated by thereal-time automated classification interface 12. As illustrated, themachine-learning observation data 52 may include a feature table 53 offeatures 54 and observations 55 that the machine learning module 24 mayinteract with. The features 54 may relate to particularcharacterizations of the log data 16. For example, the features 54 mayinclude a feature “F1” corresponding to an anchor microphone activationstatus, a feature “F2” corresponding to a camera #1 activation status, afeature “F3” corresponding to a tally light activation status, etc.Other features from the log data 16 may also be provided as features.For example, graphics channels, newsroom system output, tally logs, etc.may be provided as features. Further, because the log data 16 areaggregated, coordination features that provide an indication of how twoor more devices are working together may be provided, such as a featurethat indicates that a broadcast content active shot from camera #1 wasrecently switched to from camera #2. As another example, thecoordination features may indicate that a certain CG message was calledup by an operator from a list serve, and the tally system 19 indicated(e.g., in the tally log) that the CG message was actually brought to air(e.g., as illustrated in rows 42 b, 42 d, and 42 e of FIG. 3A and FIG.3B). There are groups of actions that may correlated based on tally logtiming, as described above. These action groups may map to ontologies,which may also provided as features 54 in the feature table 53.

As such, each column 56 relates to a specific feature. The observations55 may relate to metadata results for particular combinations offeatures 54 and/or observations of the control, suggestions, and/orreporting for the broadcast content for a given combination of features54. For example, from a metadata context, the observation “O1” couldrepresent combinations of features 54 present when an anchor is presentin the active shot (or when a particular Anchor X is in the activeshot). Further, from a control, suggestion, and/or reporting context,the observations 55 may include an observation “O1” corresponding to avideo clip inserted in the broadcast content, an observation “O2”corresponding to a graphic presentation inserted in the broadcastcontent, an observation “O3” corresponding to a text file of speechtranslation, etc. As such, each row 58 relates to a specificobservation.

Each row 58 provides combinations of the features (e.g., F1, F2, F3 . .. Fn) and an indication of whether or not the feature is present at aparticular time or during a particular period of time in the content. Aspecific feature includes a “1” if data relating to the feature ispresent and a “0” if the feature is not present. For example, in anobservation “O1”, the feature “F1”, related to an anchor microphone, isset to “1”, indicating that the anchor microphone is active. Further, inthe observation “O1” related to a camera #2 (e.g., “F2”), the “0”indicates that the camera #2 is off. All of the features 54 andobservations 55 in the feature table 53 are expressed in this manner toenable the machine learning module 24 to predict derived metadata and/orcontrol actions, suggestions, and/or reporting content, usingcombinations of features present in log data 16, without requiring humanevaluation or subjectivity.

As indicated above, each of the observations may pertain to a specifictime or a period of time. For example, an observation may pertain to atime indicated in the tally log, and a row 58 associated with thatobservation may indicate whether data relating to particular features(e.g., as indicated in columns 56) was present at that time. Anobservation may also relate to a period of time, such as a period oftime that occurs between two times indicated in the tally log data 16 a.For instance, feature “F1” may relate to a status as to whether agraphics generator has received a request for a particular graphic, andfeature “F2” may pertain to whether the graphics generator has providedthe requested graphic.

As mentioned above, the real-time automated system 10 may provideenhancement to automated control, automated suggestions, and/orautomated reporting based on the log data 16. FIGS. 5-8 below illustrateexamples of manners that the real-time automated system 10 may generateand provide automated control, automated suggestions, and/or automatedreporting relating to the broadcast content. FIG. 5 is a schematicillustrating an example of the real-time automated classification system10 of FIG. 1 providing an automated suggestion relating to broadcastcontent. In the illustrated embodiment, a news reporter 60 is reportingnews 62 relating to stock market. The log data 16 is received and/oraccumulated by the real-time automated classification system 10 asindicated by an arrow 64. The real-time automated classification system10 may derive metadata 39 based on the log data 16. In the illustratedexample, the metadata 39 may include information that allows thereal-time automated classification system 10 to determine that aparticular portion of the broadcast content (e.g., news 62) includesinformation indicating “stock values have gone up in the last quarter.”In response to such determination, the real-time automatedclassification system 10 may provide (arrow 70) suggestions 65 relevantto what is happening in the broadcast content. For example, thereal-time automated classification system 10 may generate and providethe suggestions, such as a graphical user interface (GUI) 66 including agraphic presentation 68 showing the stock performance or trend in thelast quarter(s).

The real-time automated classification system 10 may provide (e.g., asindicated by an arrow 70) the suggestions 66 to be reviewed or approvedprior to incorporation of the suggestions 65 in the broadcast content.For example, the suggestions 65 may be received at a control room 72,which has an option of accepting or denying the suggested graphicpresentation 68. In response to the suggestions 65 being accepted, thereal-time automated classification system 10 may automate instructionsto incorporate or insert B-roll content 75 in the broadcast content asindicated by an arrow 74. As such, audiences viewing the broadcastcontent may receive the news 62 as well as the B-roll content 75relating to the news 62 in real-time. In some embodiments, upongeneration of the suggestions 65, the real-time automated classificationsystem 10 may automate instructions to incorporate the suggestions 65 inthe broadcast content without a confirmation (e.g., confirmation ofacceptance from an authorized personnel or the control room 72). Thatis, the real-time automated classification system 10 may alter thebroadcast content by automatically incorporating the suggestions 65 intothe broadcast content. It should be noted that the processes set forthabove in FIG. 5 may occur real-time or substantially real-time withrespect to the live broadcasting.

FIG. 6 is a schematic illustrating an example of the real-time automatedclassification system 10 of FIG. 1 providing an automated clip relatingto broadcast content. In the illustrated embodiment, the log data 16 isreceived and/or accumulated by the real-time automated classificationsystem 10 as indicated by an arrow 80. The real-time automatedclassification system 10 may derive the metadata 39 based on the logdata 16. In the illustrated example, the metadata 39 may includeinformation that allows the real-time automated classification system 10to determine that a particular portion of the broadcast content includesa “key moment” or a “highlight moment.” For example, in the case of livebroadcasting a sports game, a scoring moment may be a key moment. Thereal-time automated classification system 10, in particular the machinelearning module 24, may be trained to recognize correlations orrelationships in the metadata 39 to recognize a key moment in thebroadcast content. For example, the machine learning module 24 maydetermine that a key moment may strongly correlate with the metadata 39including information indicating a particular camera or multiple camerashave zoomed to a particular area or player(s) on the field, informationindicating increasing cheering volume of the audiences, informationindicating a commentator is commenting relating to a scoring moment,etc. In response to determining a key moment, the real-time automatedclassification system 10 may automate instructions to auto-clip aportion (e.g., one or more frames) of the broadcast contentcorresponding to the determined key moment. For example, the real-timeautomated classification system 10 may determine a start time 82 and astop time 84 in an original or un-clipped live broadcast content 86,such that the content between the start time 82 and the stop time 84corresponds to the key moment. The real-time automated classificationsystem 10 may generate (as indicated by an arrow 90) auto-clippedsegments 88 that correspond to the key moment (e.g., from the start time82 to the stop time 84). The real-time automated classification system10 may automate instructions to incorporate or insert the auto-clippedsegments 88 as “re-play” or “highlight” at a suitable time (e.g., ashort period after the scoring moment, as the commentator is commentingon the scoring moment). As such, audiences viewing the broadcast contentmay receive the live sports broadcast as well as the auto-clippedsegments 88 as re-play or highlight of the key moment. It should benoted that the processes set forth above in FIG. 6 may occur real-timeor substantially real-time with respect to the live broadcasting.

In another aspect, it should be noted that the auto-clipped segments 88may be tagged for distribution (e.g., via an over-the-top (OTT) mediaprovider, a mobile network, enhanced cable). For example, theauto-clipped segments 88 may be provided via a digital distributionnetwork (e.g., an OTT media provider, a mobile network, enhanced cable)while the program from which the auto-clipped segments were taken isstill airing. Moreover, some of the auto-clipped segments 88 may beprovided before all of the auto-clipped segments have been identified.For instance, the real-time automated classification system 10 maydetermine that a topic is being discussed during a live broadcast andtreat the discussion of the topic as a key moment. Accordingly, thereal-time automated classification system 10 may auto-clip segments fromthe content as the content is occurring live. In another aspect, whilethe content is still airing and while the real-time automatedclassification system 10 continues to generate auto-clipped segments 88related to the topic, already identified segments from the auto-clippedsegments 88 may tagged for distribution and distributed via a digitaldistribution network. Viewers of the distributed auto-clipped segments88 may interact with the content (e.g., rewind) while the topic is stillbeing discussed on the live broadcast and before at least a portion ofthe segments of content that the real-time automated classificationsystem 10 ultimately determines are related to the topic are included asauto-clipped segments 88. In yet another aspect, the real-time automatedclassification system 10 may also tag auto-clipped 88 segments withdescriptors such as a sports highlight or a key news event of the day,which may enable the system to automatically search for a particularcategory of tags for automated distribution via the different channelsmentioned at a designated time.

FIG. 7 is a schematic illustrating an example of the real-time automatedclassification system 10 of FIG. 1 providing an automated reportrelating to broadcast content. In the illustrated embodiment, the logdata 16 is received and/or accumulated by the real-time automatedclassification system 10 as indicated by an arrow 100. The real-timeautomated classification system 10 may derive the metadata 39 based onthe log data 16. For example, closed captioning logs or teleprompterlogs may provide an indication of text that has been spoken and/or willbe spoken during the content capture. This log data 16 may be used toidentify a particular context or topic of the content.

In the illustrated example, the metadata 39 may include information thatallows the real-time automated classification system 10 to determinethat particular one or more portions of the broadcast content includeinformation 102 relating to a specific target 104. In this case, thespecific target 104 is “Company A”, and the information 102 may includeany suitable information about Company A, such as market shareinformation, performance information, industry segment information ofCompany A, etc. In response to such determination, the real-timeautomated classification system 10 may automate instructions to generate(as indicated by an arrow 106) a report 108 or broadcast or digitalgraphic about Company A. In some embodiments, the real-time automatedclassification system 10 may automate instructions to organize,summarize, and present information in the broadcast content in asuitable manner. For example, a news anchor may speak (or receiveteleprompter text) about Company A's practices in different segmentsduring the live broadcasting, and the real-time automated classificationsystem 10 may organize, summarize, or transform the content spoken (orthe teleprompter text) by the news anchor into visual representations110, e.g., graphical representations, pictures, images, plots, tables,texts, in the generated report 108. In some embodiments, the real-timeautomated classification system 10 may automate instructions to searchinformation (e.g., historical broadcast content, searchable informationon Internet, information in the broadcast content) and organize,summarize, or transform the information found in the search into visualrepresentations 112, e.g., graphical representations, pictures, images,plots, tables, texts, in the generated report 108. The real-timeautomated classification system 10 may incorporate or insert the report108 into the broadcast content at a suitable time during broadcasting,distribute or send the report 108 to authorized personnel (e.g., a newsreporter, a producer, an editor of the live broadcast), or both.Moreover, the real-time automated classification system 10 may generatethe report 108 based on user input. For example, a user may requestreports be generated based on a keyword. More specifically, thereal-time automated classification system 10 may generate contentspecific to the keyword. For instance, a user may request a report abouta specific stock listing, television program, person (e.g., actor,anchor, guest), or other type of information associated with content.The real-time automated classification system 10 may filter data basedon the request and provide the report 108. Additionally, there are alsodigital applications to the automated reports 108, content, andgraphics. For example, information and charts of content can bedistributed on an enhanced landing page of clipped videos, such as avideo made of the auto-clipped segments 88. It should be noted that theprocesses set forth above in FIG. 7 may occur real-time or substantiallyreal-time with respect to the live broadcasting.

FIG. 8 is a schematic illustrating an example of the real-time automatedclassification system 10 of FIG. 1 providing an automated controlrelating to broadcast content. In the illustrated embodiment, the logdata 16 is received and/or accumulated by the real-time automatedclassification system 10 as indicated by an arrow 120. The real-timeautomated classification system 10 may derive the metadata 39 based onthe log data 16. The metadata 39 may include information that allows thereal-time automated classification system 10 to determine originalitiesof the different log data 16 (e.g., the log data 16 from microphones,audio devices, cameras). In addition, the metadata 39 may includeinformation that allows the real-time automated classification system 10to determine that a portion of the log data 16 is more important or hasa higher priority than another portion of the log data 16 depending onwhat is happening in the broadcast content. For example, the real-timeautomated classification system 10 may determine that a portion of thelog data 16 corresponds to verbiage audio signals 122, e.g., audiosignals from one or more microphones used by a live show host. Thereal-time automated classification system 10 may determine that anotherportion of the log data 16 corresponds to background audio signals 124,e.g., audio signals from one or more audio devices disposed in the livebroadcast, such as music cues or sound effects.

Further, the real-time automated classification system 10 may determinethat for particular time segments of the broadcast content, the verbiageaudio signals 122 may be more important than the background audiosignals 124. For example, for the particular time segments that the liveshow host is speaking, the verbiage audio signals 122 may be moreimportant than the background audio signals 124, like music. Upon suchdetermination, the real-time automated classification system 10 maygenerate control inputs 126 and send (indicated by an arrow 128) thecontrol inputs 126 to a suitable control system configured to eliminatethe background sound, which enables further machine learning processeslike Natural Language Processing to work more efficiently. Inparticular, the control inputs 126 are configured to cause the suitablecontrol system to adjust processing of the verbiage audio signals 122with respect to the background audio signals 124 corresponding to whatis happening in the broadcast content. For example, the control inputs126 may be configured to cause a suitable audio control system toperform mathematical manipulation, scaling, and/or transformation on theverbiage audio signals 122 and/or the background audio signals 124, suchthat when the live show host is speaking, the host's voice is clear(e.g., not distracted by the background sound/voice) in the broadcastcontent. It should be noted that the processes set forth above in FIG. 8may occur real-time or substantially real-time with respect to the livebroadcasting.

FIG. 9 is a schematic illustrating an example of the real-time automatedclassification system 10 of FIG. 1 providing an automated text filerelating to broadcast content. In the illustrated embodiment, the logdata 16 is received and/or accumulated by the real-time automatedclassification system 10 as indicated by an arrow 140. The real-timeautomated classification system 10 may derive the metadata 39 based onthe log data 16. The metadata 39 may include information that allows thereal-time automated classification system 10 to determine one or moreaudio files to be translated and/or corresponding captions to begenerated. For example, based on information in the metadata 39, thereal-time automated classification system 10 may determine content in aparticular isolated audio track to be translated and/or correspondingcaptions to be generated. In response to such determination, thereal-time automated classification system 10 may automate instructions(as indicated by an arrow 142) to translate and/or generate (asindicated by an arrow 144) captions or a text file 146 for thecorresponding content of the particular isolated audio track. Forexample, an automated speech translation system may be used to generatetranslated captions (e.g., the text file 146) for the correspondingspeech. For example, an automated speech recognition system may be usedto generate captions (e.g., the text file 146) for the correspondingspeech. The real-time automated classification system 10 may incorporateor insert the text file 146 in the broadcast content. It should be notedthat the processes set forth above in FIG. 9 may occur real-time orsubstantially real-time with respect to the live broadcasting.

The real-time automated classification system 10 enables metadata to beutilized for several other purposes. For instance, metadata may enableenhanced searches to be performed. For example, as described above, logdata 16 may be associated with metadata or “tags.” Users may requestcontent or items associated with content (e.g., log data 16, graphics,text, information about the content). The real-time automatedclassification system 10 may search for tags based on the user requestand return content or other data (e.g., log data 16, graphics) that areassociated with a tag that the real-time classification system 10searched for. As another example, while determining an automatedsuggestion relating to broadcast content, the real-time classificationsystem 10 may search through databases of content, graphics, log data16, and other data. The real-time classification system 10 may make asuggestion based at least in part on data tags associated with broadcastcontent for which the suggestion is made. For example, the real-timeclassification system 10 may determine (e.g., based on log data 16associated with a teleprompter or speech recognition analysis of audiofrom the broadcast) that a company is being discussed as part of abroadcast related to the stock market. A data tag may exist for thecompany. Accordingly, the real-time classification system 10 maydetermine other content associated with the same data tag (e.g.,graphics or charts associated with the company from prior broadcasts),and suggest that the other content be incorporated into the broadcast.

As another example of how data may be correlated and how metadata may beutilized, the real-time automated classification system 10 may determinecorrelations between companies, industries, and commodities. Forexample, a data library may include information regarding companies,industries, and commodities. The real-time automated classificationsystem 10 may analyze the data regarding the companies, industries, andcommodities and determine whether various companies, industries, andcommodities are interrelated. For example, a company may manufacturecell phones. The real-time automated classification system 10 maydetermine that the company makes the cell phone (e.g., based on log data16 from a CG log, newsroom computer system log (e.g., a prompter log),searchable information on the Internet) and add metadata tags toindicate that the company makes and sells cell phones and that thecompany is in a particular industry or sector (e.g., technology).

Additionally, real-time automated classification system 10 may determineother companies that may be related to the company. For example, thereal-time automated classification system 10 may search log data 16 fromprevious broadcasts to determine potential competitors, customers,and/or suppliers of the company. For example, a previous broadcast mayhave related to a company that makes mobile processors. Based on logdata 16 associated with such a broadcast, the real-time automatedclassification system 10 may determine that the mobile processor companycould be a supplier to the cell phone manufacturing company.

Expanding on this example, the real-time automated classification system10 may determine an entire product chain related to a product, such asthe cell phone discussed above. For instance, the real-time automatedclassification system 10 may add data tags to indicate various parts orcommodities typically used in cell phones, such as screens, variouscircuitry, commodities from which components in the cell phone are made(e.g., lithium, silicon). The real-time automated classification system10 may determine log data 16 associated with such companies andcommodities, and add data tags to indicate that the companies andcommodities may be, or are, in the supply chain for the product (e.g.,the cell phone).

The real-time automated classification system 10 may make suggestionsbased on metadata indicative of connections between companies and/orcommodities. For instance, based on log data 16 (e.g., CG log data 16 b,newsroom computer system log data 16 c, tally log data 16 a) associatedwith a broadcast, the real-time automated classification system 10 maydetermine that a company is being discussed. Based on thisdetermination, the real-time automated classification system 10 maysuggest content be added for one or more companies or commodities in thesame supply chain as the company being discussed.

Additionally, the real-time automated classification system 10 mayenable data streams to be established based on the metadata. Forexample, as content is broadcasted, the real-time automatedclassification system 10 may determine that the content is related to aparticular subject. For example, in a broadcast regarding investing, thecontent may be related to a company. The real-time automatedclassification system 10, as indicated above, may determine that thecontent is related to the company based on the log data 16 associatedwith the broadcast. When the real-time automated classification system10 makes determines that the broadcast is related to the company, thereal-time automated classification system 10 may generate metadata(e.g., data tags) indicating that the broadcast is related to thecompany. More specifically, the real-time automated classificationsystem 10 may determine a portion of the broadcast related to thecompany (e.g., specific frames of content from the broadcast relating tothe company) to be a key moment. Accordingly, the real-time automatedclassification system 10 may determine supply chains related tocompanies, products made by companies, and commodities. Furthermore thereal-time automated classification system 10 may provide suggestionsrelated to the determined supply chains in real-time or substantiallyreal-time with respect to the live broadcasting.

Moreover, a data stream, such as a data feed (e.g., a Rich Site Summary(RSS) feed), specific to the company may exist or be established. Ascontent is broadcasted and identified as being related to the company,the real-time automated classification system 10 may automatically addthe content, portions of the content, or other information related tothe company, to the data stream in real-time or substantially real-timewith respect to the live broadcasting. For example, data related to thecontent may include a stock chart or information about a company orcommodity in the same supply chain as the company. The real-timeautomated classification system 10 may include such information in thedata steam in addition to content or portions of the content frombroadcast.

In another embodiment, the real-time automated classification system 10may enable generation of movie clips based on recorded video content. Inan aspect, the real-time automated classification system 10 may generateclips based on when a particular actor appears or when a group of actorappears in the video. In another aspect, the real-time automatedclassification system 10 may generate clips based on when one or moreactors appears in a scene and an event occurs (e.g., an explosion). Theevent may be tagged by the real-time automated classification system 10.In another aspect, the real-time automated classification system 10 maygenerate clips when a particular actor appears in a scene and aparticular sound or portion of the soundtrack is played back. In anotheraspect, the real-time automated classification system 10 may generateclips when an actor or group of actors recites a phrase or series ofphrases.

While only certain features of the present disclosure have beenillustrated and described herein, many modifications and changes willoccur to those skilled in the art. It is, therefore, to be understoodthat the appended claims are intended to cover all such modificationsand changes as fall within the true spirit of the present disclosure.

The invention claimed is:
 1. A tangible, non-transitory machine-readablemedium comprising machine-readable instructions that, when executed byone or more processors, cause the one or more processors to: receive logdata generated during generation of broadcast content from one or moredevices associated with generation of the broadcast content, wherein thelog data comprises time code information, text information, audio logdata, real-time graphic system log data, tally log data, switcher logdata, newsroom system log data, MOS gateway log data, replay log data,character generator log data, video playout log data, or a combinationthereof; generate metadata associated with the broadcast content fromone or more features derived from the log data; and perform, duringbroadcasting of the broadcast content and based on the metadata,automated control, automated suggestions, automated reporting, or acombination thereof.
 2. The non-transitory machine-readable medium ofclaim 1, wherein the log data comprises the time code information, thetext information, the audio log data, the real-time graphic system logdata, or a combination thereof.
 3. The non-transitory machine-readablemedium of claim 1, wherein the log data comprises the tally log data,wherein the tally log data is indicative of an on-air status of the oneor more devices.
 4. The non-transitory machine-readable medium of claim1, wherein: the metadata comprises an indication of one or morehighlight moments of the broadcast content; and the machine-readableinstructions, when executed, cause the one or more processors togenerate a clip corresponding to the one or more highlight moments ofthe broadcast content.
 5. The non-transitory machine-readable medium ofclaim 4, wherein the machine-readable instructions, when executed, causethe one or more processors to perform automated suggestions byincorporating the clip into the broadcast content.
 6. The non-transitorymachine-readable medium of claim 1, wherein the machine-readableinstructions, when executed, cause the one or more processors to performautomated suggestions by: generating a suggestion for a graphicalrepresentation corresponding to a current portion of the broadcastcontent; and incorporating the graphical representation in the broadcastcontent.
 7. The non-transitory machine-readable medium of claim 1,wherein the machine-readable instructions, when executed, cause the oneor more processors to derive the one or more features by characterizingthe log data, correlating the log data, or both.
 8. Amachine-implemented method for providing real-time automated logging ofbroadcast content, comprising: receiving log data generated duringgeneration of the broadcast content from one or more devices associatedwith generation of the broadcast content, wherein the log data comprisestime code information, text information, audio log data, real-timegraphic system log data, tally log data, switcher log data, newsroomsystem log data, MOS gateway log data, replay log data, charactergenerator log data, video playout log data, or a combination thereof;generating, from one or more features derived from the log data,metadata comprising data indicative of content within the broadcastcontent; and performing, during broadcasting of the broadcast contentand based on the metadata, automated control, automated suggestions,automated reporting, or a combination thereof.
 9. Themachine-implemented method of claim 8, wherein: the log data comprisesthe audio log data, real-time the graphic system log data, the tally logdata, the switcher log data, the newsroom system log data, MOS gatewaylog data, the replay log data, the character generator log data, thevideo playout log data, or a combination thereof; and the tally log datais indicative of an on-air status of the one or more devices.
 10. Themachine-implemented method of claim 8, wherein the log data or metadatacomprises data regarding a person appearing in the broadcast content.11. The machine-implemented method of claim 10, wherein performingautomated suggestions comprises: generating a suggestion for a picture,plot, table, or text based on the person; and incorporating the picture,plot, table, or text into the broadcast content.
 12. Themachine-implemented method of claim 11, wherein the picture, plot,table, or text comprises information about the person or a topicdiscussed by the person.
 13. The machine-implemented method of claim 10,comprising: generating a video clip from previously-captured content inwhich the person appears; and performing automated suggestions byincorporating the video clip into the broadcast content.
 14. Themachine-implemented method of claim 8, comprising determining thecontent within the broadcast content.
 15. The machine-implemented methodof claim 8, wherein performing automated suggestions comprisesgenerating textual or graphical data associated with verbiage present ona verbiage channel.
 16. A real-time automated classification system,comprising: a machine learning module implemented at least partially byone or more processors executing, wherein the machine learning module isconfigured to: receive log data generated during generation of broadcastcontent from one or more devices associated with generation of thebroadcast content, wherein the log data comprise time code information,text information, audio log data, real-time graphic system log data,tally log data, switcher log data, newsroom system log data, MOS gatewaylog data, replay log data, character generator log data, video playoutlog data, or a combination thereof; derive one or more features from thelog data by characterizing the log data, correlating the log data, orboth; generate metadata associated with the broadcast content from theone or more features derived from the log data; and perform, duringbroadcasting of the broadcast content, automated control, automatedsuggestions, automated reporting, or a combination thereof, based on themetadata.
 17. The real-time automated classification system of claim 16,wherein the log data comprises the switcher log data, the newsroomsystem log data, the MOS gateway log data, the replay log data, thecharacter generator log data, the video playout log data, or acombination thereof.
 18. The real-time automated classification systemof claim 16, wherein the machine learning module is configured to betrained based on records of previous live broadcast content.
 19. Thereal-time automated classification system of claim 16, comprising acontrol system having one or more processors configured to alter thebroadcast content by automatically incorporating the automatedsuggestions into the broadcast content.
 20. The real-time automatedclassification system of claim 19, wherein the one or more processors ofthe control system are configured to: request an approval of predictedautomated suggestions, the predicted automated reporting, or both; andin response to receiving the approval, incorporate the automatedsuggestions, the automated reporting, or both in the broadcast content.